Rather than derive closed form modifications to bottleneck features, several works propose to instead predict feature transformations using a trained CNN. Li et al. [1]} proposed learning to predict an affine transformation and bias, resulting in a feature transform with the same form as the (WCT), but which can be computed far more efficiently (since it is generated via a feed-forward pass, rather using an expensive eigendecomposition). Jing et al. [2]} proposed instead directly predicting the parameters of a style-driven convolution and bias layer. This formulation allowed them to take advantage of other advances in architecture such as deformable convolutions [3]}.
